"Pyramid Deep dehazing": An unsupervised single image dehazing method using deep image prior

Single image dehazing remove haze from degraded images and recover clean scenes. Prior based Methods can achieve great results on some haze images, but their performance is limited by the handcraft prior itself. Recently, many learning-based approaches has been proposed. Most of these modules rely on matching clean and haze images for training. However, such kind of real world data can be hard to get. Also the domain shift between training and testing data may affect the results. Some unsupervised methods have been proved to work on haze scenes but they still rely on handcraft priors to guide the training. In this paper, we proposed an Unsupervised Single Image Dehazing method using internal learning based on the optical model of haze and other haze-like degradation images. A Pyramid Deep Image strategy is used to gradually generate clean background. The entire training doesn't need any extra data or handcraft prior, and only needs the testing image itself. The proposed method is able to deal with different kinds of haze images including other haze-like degradation (like nighttime images and underwater images).